| 1. |
- Breinl, Korbinian, et al.
(författare)
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Simulating daily precipitation and temperature: a weather generation framework for assessing hydrometeorological hazards
- 2015
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Ingår i: Meteorological Applications. - : Wiley. - 1350-4827 .- 1469-8080. ; 22:3, s. 334-347
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Tidskriftsartikel (refereegranskat)abstract
- Stochastic weather generators simulate synthetic weather data while maintaining statistical properties of the observations. A new semi-parametric algorithm for multi-site precipitation has been published recently by Breinl et al. (2013), who used a univariate Markov process to simulate precipitation occurrence at multiple sites for two small rain gauge networks. Precipitation amounts were simulated in a two-step process by first resampling observations and then sampling and reshuffling of parametric precipitation amounts. In the present study, the precipitation model by Breinl et al. (2013, J. Hydrol. 498: 23–35) is implemented in a weather generation framework for daily precipitation and temperature. It is extended to a considerably larger gauge station network of 19 stations and further improved to reduce the duplication of historical records in the simulation. Autoregressive-moving-average models (ARMA) are used to simulate mean daily temperature at three sites. Power transformations reduce the bias of simulated temperature extremes. Precipitation amounts are simulated by means of hybrid distributions consisting of a Weibull distribution for low precipitation amounts and a generalized Pareto distribution (GPD) for moderate and extreme precipitation amounts. The proposed weather generator is particularly suitable for assessing hydrometeorological hazards such as flooding as it reproduces the spatial variability of precipitation very well and can generate unobserved extremes.
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| 2. |
- Breinl, Korbinian, et al.
(författare)
-
Stochastic generation of multi-site daily precipitation for applications in risk management
- 2013
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Ingår i: Journal of Hydrology. - : Elsevier BV. - 0022-1694 .- 1879-2707. ; 498, s. 23-35
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Tidskriftsartikel (refereegranskat)abstract
- Unlike single-site precipitation generators, multi-site precipitation generators make it possible to reproduce the space–time variation of precipitation at several sites. The extension of single-site approaches to multiple sites is a challenging task, and has led to a large variety of different model philosophies for multi-site models. This paper presents an alternative semi-parametric multi-site model for daily precipitation that is straightforward and easy to implement. Multi-site precipitation occurrences are simulated with a univariate Markov process, removing the need for individual Markov models at each site. Precipitation amounts are generated by first resampling observed values, followed by sampling synthetic precipitation amounts from parametric distribution functions. These synthetic precipitation amounts are subsequently reshuffled according to the ranks of the resampled observations in order to maintain important statistical properties of the observation network. The proposed method successfully combines the advantages of non-parametric bootstrapping and parametric modeling techniques. It is applied to two small rain gauge networks in France (Ubaye catchment) and Austria/Germany (Salzach catchment) and is shown to well reproduce the observations. Limitations of the model relate to the bias of the reproduced seasonal standard deviation of precipitation and the underestimation of maximum dry spells. While the lag-1 autocorrelation is well reproduced for precipitation occurrences, it tends to be underestimated for precipitation amounts. The model can generate daily precipitation amounts exceeding the ones in the observations, which can be crucial for risk management related applications. Moreover, the model deals particularly well with the spatial variability of precipitation. Despite its straightforwardness, the new concept makes a good alternative for risk management related studies concerned with producing daily synthetic multi-site precipitation time series.
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